{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cs224 \n",
      "last updated: 2020-06-30 \n",
      "\n",
      "CPython 3.6.10\n",
      "IPython 7.15.0\n",
      "\n",
      "numpy 1.18.1\n",
      "xarray 0.15.1\n",
      "scipy 1.4.1\n",
      "pandas 1.0.4\n",
      "sklearn 0.22.1\n",
      "matplotlib 3.2.1\n",
      "seaborn 0.10.1\n",
      "pymc3 3.9.1\n",
      "lifelines 0.24.11\n",
      "rpy2 3.3.3\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'cs224' -u -d -v -p numpy,xarray,scipy,pandas,sklearn,matplotlib,seaborn,pymc3,lifelines,rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np, scipy, scipy.stats as stats, scipy.special, scipy.misc, pandas as pd, matplotlib.pyplot as plt, seaborn as sns, xarray as xr\n",
    "import matplotlib as mpl\n",
    "\n",
    "import pymc3 as pm\n",
    "\n",
    "import theano as thno\n",
    "import theano.tensor as T\n",
    "\n",
    "import datetime, time, math\n",
    "from dateutil import relativedelta\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "SEED = 41\n",
    "np.random.seed(SEED)\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "# pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "np.set_printoptions(edgeitems=10)\n",
    "np.set_printoptions(linewidth=1000)\n",
    "np.set_printoptions(suppress=True)\n",
    "np.core.arrayprint._line_width = 180\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:70% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "from IPython.display import display_html\n",
    "def display_side_by_side(*args):\n",
    "    html_str=''\n",
    "    for df in args:\n",
    "        if type(df) == np.ndarray:\n",
    "            df = pd.DataFrame(df)\n",
    "        html_str+=df.to_html()\n",
    "    html_str = html_str.replace('table','table style=\"display:inline\"')\n",
    "    # print(html_str)\n",
    "    display_html(html_str,raw=True)\n",
    "\n",
    "CSS = \"\"\"\n",
    ".output {\n",
    "    flex-direction: row;\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "def display_graphs_side_by_side(*args):\n",
    "    html_str='<table><tr>'\n",
    "    for g in args:\n",
    "        html_str += '<td>'\n",
    "        html_str += g._repr_svg_()\n",
    "        html_str += '</td>'\n",
    "    html_str += '</tr></table>'\n",
    "    display_html(html_str,raw=True)\n",
    "    \n",
    "\n",
    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport covid19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Estimates of the severity of coronavirus disease 2019: a model-based analysis](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)\n",
    "    * duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2)\n",
    "    * to hospital discharge to be 24·7 days (22·9–28·1).\n",
    "* [Estimating SARS-COV-2 infections](https://observablehq.com/@danyx/estimating-sars-cov-2-infections)\n",
    "    * it takes on average 23 days from infection to death.\n",
    "    * ~5 days from infection to symptoms and ~18 days from symptoms to death so we arrive at a default value of 23 days from infection to death\n",
    "* [Characteristics of COVID-19 patients dying in Italy Report based on available data on March 20th, 2020](https://www.epicentro.iss.it/coronavirus/bollettino/Report-COVID-2019_20_marzo_eng.pdf)\n",
    "    * Median number of days between onset of symptoms to death: 8\n",
    "    * Median number of days between onset of symptoms to hospitalization: 4\n",
    "* [Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext)\n",
    "    * Time from illness onset to hospital admission, days\t11·0 (8·0–14·0)\n",
    "* [Coronavirus: how quickly do COVID-19 symptoms develop and how long do they last?](https://patient.info/news-and-features/coronavirus-how-quickly-do-covid-19-symptoms-develop-and-how-long-do-they-last)\n",
    "    * ~5 days from infection to symptoms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Gamma distribution](https://en.wikipedia.org/wiki/Gamma_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f89bdc9a6a0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0.0,30.0,1000)\n",
    "y = covid19.gamma_dist.pdf(x)\n",
    "fig=plt.figure(figsize=(14, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax = plt.subplot(111)\n",
    "ax.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.893998550302967, 25.668385368738527, 0.11661697412505936)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19.gamma_loc,covid19.gamma_k,covid19.gamme_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5.51, 5.36, 5.67, 3.3, 5.433614364053376)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'China', None, 0\n",
    "# cfr_estimate, timeshift = covid19.calculate_delay_between_new_cases_and_death(country_name, first_date=first_date, init_add=init_add)\n",
    "# print(cfr_estimate, timeshift)\n",
    "# loc = max(timeshift - (gamma_mean - gamma_loc), 0.0)\n",
    "china_mortality_analysis = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add)\n",
    "china_mortality_analysis.fit()\n",
    "print(china_mortality_analysis.death_rate())\n",
    "china_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>84161</td>\n",
       "      <td>79400</td>\n",
       "      <td>4638</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>84160</td>\n",
       "      <td>79404</td>\n",
       "      <td>4638</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>84171</td>\n",
       "      <td>79415</td>\n",
       "      <td>4638</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>84177</td>\n",
       "      <td>79420</td>\n",
       "      <td>4638</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-06</th>\n",
       "      <td>84186</td>\n",
       "      <td>79424</td>\n",
       "      <td>4638</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-06-02      84161      79400   4638              7              2          0\n",
       "2020-06-03      84160      79404   4638             -1              4          0\n",
       "2020-06-04      84171      79415   4638             11             11          0\n",
       "2020-06-05      84177      79420   4638              6              5          0\n",
       "2020-06-06      84186      79424   4638              9              4          0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.df_lifelines_individual.observed_death.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.wbf.print_summary()\n",
    "\n",
    "# expected_life_time = china_mortality_analysis.wbf.lambda_ * scipy.special.gamma(1 + 1 / china_mortality_analysis.wbf.rho_)\n",
    "# expected_life_time/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.death_rate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>182954</td>\n",
       "      <td>169094</td>\n",
       "      <td>8563</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>183304</td>\n",
       "      <td>169094</td>\n",
       "      <td>8602</td>\n",
       "      <td>350</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>183629</td>\n",
       "      <td>169094</td>\n",
       "      <td>8635</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>183864</td>\n",
       "      <td>169094</td>\n",
       "      <td>8658</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-06</th>\n",
       "      <td>183952</td>\n",
       "      <td>169094</td>\n",
       "      <td>8673</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "index                                                                           \n",
       "2020-06-02     182954     169094   8563            223              0          8\n",
       "2020-06-03     183304     169094   8602            350              0         39\n",
       "2020-06-04     183629     169094   8635            325              0         33\n",
       "2020-06-05     183864     169094   8658            235              0         23\n",
       "2020-06-06     183952     169094   8673             88              0         15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alternative_germany_data = covid19.get_rki_df()\n",
    "alternative_germany_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 4.88 (lower: 4.78, upper:4.98), CFR via shift and scale: 4.35 (time delay between infection and death: 24.08 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'Germany', pd.to_datetime('2020-03-09'), 0.0\n",
    "germany_mortality_analysis = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add, df=alternative_germany_data) \n",
    "germany_mortality_analysis.fit()\n",
    "print(germany_mortality_analysis.print_death_rate())\n",
    "germany_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# germany_mortality_analysis.ll.shift_and_scale_popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# germany_mortality_analysis.ll.plot_lead_lag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1517: UserWarning:Exception in curve_fit: Optimal parameters not found: Number of calls to function has reached maxfev = 600./ None\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "germany_mortality_analysis.plot_infection_and_death_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>182954</td>\n",
       "      <td>169094</td>\n",
       "      <td>8563</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>183304</td>\n",
       "      <td>169094</td>\n",
       "      <td>8602</td>\n",
       "      <td>350</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>183629</td>\n",
       "      <td>169094</td>\n",
       "      <td>8635</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>183864</td>\n",
       "      <td>169094</td>\n",
       "      <td>8658</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-06</th>\n",
       "      <td>183952</td>\n",
       "      <td>169094</td>\n",
       "      <td>8673</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "index                                                                           \n",
       "2020-06-02     182954     169094   8563            223              0          8\n",
       "2020-06-03     183304     169094   8602            350              0         39\n",
       "2020-06-04     183629     169094   8635            325              0         33\n",
       "2020-06-05     183864     169094   8658            235              0         23\n",
       "2020-06-06     183952     169094   8673             88              0         15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8977.0</td>\n",
       "      <td>8673</td>\n",
       "      <td>304.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1092.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0          8977.0         8673        304.0             831.0                     40.0          21                        1092.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8681.0</td>\n",
       "      <td>8259</td>\n",
       "      <td>422.0</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2294.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0          8681.0         8259        422.0            1746.0                     83.0          21                        2294.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1517: UserWarning:Exception in curve_fit: `x0` is infeasible./ [663.91939956   0.12216088  -3.55429014]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 3.88 (lower: 3.6, upper:4.19), CFR via shift and scale: 3.31 (time delay between infection and death: 14.35 days)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'Austria', pd.to_datetime('2020-03-12'), 600\n",
    "austria_mortality_analysis = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add)\n",
    "austria_mortality_analysis.fit()\n",
    "print(austria_mortality_analysis.print_death_rate())\n",
    "austria_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>16321</td>\n",
       "      <td>14678</td>\n",
       "      <td>632</td>\n",
       "      <td>52</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>16353</td>\n",
       "      <td>14882</td>\n",
       "      <td>633</td>\n",
       "      <td>32</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>16404</td>\n",
       "      <td>14951</td>\n",
       "      <td>633</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>16436</td>\n",
       "      <td>15005</td>\n",
       "      <td>635</td>\n",
       "      <td>32</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>16486</td>\n",
       "      <td>15037</td>\n",
       "      <td>639</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-05-19      16321      14678    632             52             64          3\n",
       "2020-05-20      16353      14882    633             32            204          1\n",
       "2020-05-21      16404      14951    633             51             69          0\n",
       "2020-05-22      16436      15005    635             32             54          2\n",
       "2020-05-23      16486      15037    639             50             32          4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austria_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>663.0</td>\n",
       "      <td>639</td>\n",
       "      <td>24.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0           663.0          639         24.0              49.0                      2.0          21                          64.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austria_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>654.0</td>\n",
       "      <td>629</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0           654.0          629         25.0              66.0                      3.0          21                          87.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austria_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1517: UserWarning:Exception in curve_fit: `x0` is infeasible./ [272.52152674   0.08205964  -8.46262351]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribute_across_cases_gamma: using uniform distribution for date: 2020-03-04 00:00:00\n",
      "CFR via Survival analysis: 2.43 (lower: 2.16, upper:2.74), CFR via shift and scale: 1.18 (time delay between infection and death: 23.12 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'Korea, South', None, 0\n",
    "south_korea_mortality_analysis = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add)\n",
    "south_korea_mortality_analysis.fit()\n",
    "# south_korea_mortality_analysis2 = covid19.MortalityAnalysis(south_korea_name, first_date=pd.to_datetime('2020-02-20'), init_add=900)\n",
    "# south_korea_mortality_analysis2.fit()\n",
    "print(south_korea_mortality_analysis.print_death_rate())\n",
    "# print(south_korea_mortality_analysis2.death_rate())\n",
    "# print(south_korea_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "south_korea_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "      <td>10962</td>\n",
       "      <td>9695</td>\n",
       "      <td>259</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-13</th>\n",
       "      <td>10991</td>\n",
       "      <td>9762</td>\n",
       "      <td>260</td>\n",
       "      <td>29</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>11018</td>\n",
       "      <td>9821</td>\n",
       "      <td>260</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-15</th>\n",
       "      <td>11037</td>\n",
       "      <td>9851</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-16</th>\n",
       "      <td>11050</td>\n",
       "      <td>9888</td>\n",
       "      <td>262</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-05-12      10962       9695    259             26             25          1\n",
       "2020-05-13      10991       9762    260             29             67          1\n",
       "2020-05-14      11018       9821    260             27             59          0\n",
       "2020-05-15      11037       9851    262             19             30          2\n",
       "2020-05-16      11050       9888    262             13             37          0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_korea_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# south_korea_mortality_analysis.prepend_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:2115: UserWarning:deaths must come after cases, ignore nonsensical fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 14.54 (lower: 14.4, upper:14.68), CFR via shift and scale: 14.72 (time delay between infection and death: -3.81 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'United Kingdom', pd.to_datetime('2020-03-05'), 800\n",
    "uk_mortality_analysis  = covid19.MortalityAnalysis(country_name)\n",
    "uk_mortality_analysis.fit()\n",
    "print(uk_mortality_analysis.print_death_rate())\n",
    "# uk_mortality_analysis2 = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add, mult=4.0)\n",
    "# uk_mortality_analysis2.fit()\n",
    "# print(uk_mortality_analysis2.death_rate())\n",
    "# print(uk_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "uk_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:2115: UserWarning:deaths must come after cases, ignore nonsensical fits\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uk_mortality_analysis.plot_infection_and_death_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>250138</td>\n",
       "      <td>1099</td>\n",
       "      <td>35422</td>\n",
       "      <td>2429</td>\n",
       "      <td>9</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>249619</td>\n",
       "      <td>1116</td>\n",
       "      <td>35786</td>\n",
       "      <td>-519</td>\n",
       "      <td>17</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>252246</td>\n",
       "      <td>1134</td>\n",
       "      <td>36124</td>\n",
       "      <td>2627</td>\n",
       "      <td>18</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>255544</td>\n",
       "      <td>1142</td>\n",
       "      <td>36475</td>\n",
       "      <td>3298</td>\n",
       "      <td>8</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>258504</td>\n",
       "      <td>1149</td>\n",
       "      <td>36757</td>\n",
       "      <td>2960</td>\n",
       "      <td>7</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-05-19     250138       1099  35422           2429              9        546\n",
       "2020-05-20     249619       1116  35786           -519             17        364\n",
       "2020-05-21     252246       1134  36124           2627             18        338\n",
       "2020-05-22     255544       1142  36475           3298              8        351\n",
       "2020-05-23     258504       1149  36757           2960              7        282"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37586.0</td>\n",
       "      <td>36757</td>\n",
       "      <td>829.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>21</td>\n",
       "      <td>15809.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         37586.0        36757        829.0           12029.0                    573.0          21                       15809.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35591.0</td>\n",
       "      <td>34546</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>14099.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>21</td>\n",
       "      <td>18530.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         35591.0        34546       1045.0           14099.0                    671.0          21                       18530.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.options.mode.chained_assignment = \"raise\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 6.16 (lower: 6.12, upper:6.19), CFR via shift and scale: 6.29 (time delay between infection and death: 1.39 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'US', pd.to_datetime('2020-02-29'), 950\n",
    "us_mortality_analysis  = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add)\n",
    "us_mortality_analysis.fit()\n",
    "# us_mortality_analysis2 = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=450, mult=1.5)\n",
    "# us_mortality_analysis2.fit()\n",
    "print(us_mortality_analysis.print_death_rate())\n",
    "# print(us_mortality_analysis2.death_rate())\n",
    "# print(us_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "us_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "us_mortality_analysis.plot_infection_and_death_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>1528568</td>\n",
       "      <td>289392</td>\n",
       "      <td>91921</td>\n",
       "      <td>20260</td>\n",
       "      <td>6214</td>\n",
       "      <td>1574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>1551853</td>\n",
       "      <td>294312</td>\n",
       "      <td>93439</td>\n",
       "      <td>23285</td>\n",
       "      <td>4920</td>\n",
       "      <td>1518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>1577147</td>\n",
       "      <td>298418</td>\n",
       "      <td>94702</td>\n",
       "      <td>25294</td>\n",
       "      <td>4106</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>1600937</td>\n",
       "      <td>350135</td>\n",
       "      <td>95979</td>\n",
       "      <td>23790</td>\n",
       "      <td>51717</td>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>1622612</td>\n",
       "      <td>361239</td>\n",
       "      <td>97087</td>\n",
       "      <td>21675</td>\n",
       "      <td>11104</td>\n",
       "      <td>1108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "2020-05-19    1528568     289392  91921          20260           6214       1574\n",
       "2020-05-20    1551853     294312  93439          23285           4920       1518\n",
       "2020-05-21    1577147     298418  94702          25294           4106       1263\n",
       "2020-05-22    1600937     350135  95979          23790          51717       1277\n",
       "2020-05-23    1622612     361239  97087          21675          11104       1108"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100011.0</td>\n",
       "      <td>97087</td>\n",
       "      <td>2924.0</td>\n",
       "      <td>32616.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>21</td>\n",
       "      <td>42866.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0        100011.0        97087       2924.0           32616.0                   1553.0          21                       42866.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91945.0</td>\n",
       "      <td>88754</td>\n",
       "      <td>3191.0</td>\n",
       "      <td>30191.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>21</td>\n",
       "      <td>39679.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         91945.0        88754       3191.0           30191.0                   1438.0          21                       39679.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>226699</td>\n",
       "      <td>129401</td>\n",
       "      <td>32169</td>\n",
       "      <td>813</td>\n",
       "      <td>2075</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>227364</td>\n",
       "      <td>132282</td>\n",
       "      <td>32330</td>\n",
       "      <td>665</td>\n",
       "      <td>2881</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>228006</td>\n",
       "      <td>134560</td>\n",
       "      <td>32486</td>\n",
       "      <td>642</td>\n",
       "      <td>2278</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>228658</td>\n",
       "      <td>136720</td>\n",
       "      <td>32616</td>\n",
       "      <td>652</td>\n",
       "      <td>2160</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>229327</td>\n",
       "      <td>138840</td>\n",
       "      <td>32735</td>\n",
       "      <td>669</td>\n",
       "      <td>2120</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "data                                                                            \n",
       "2020-05-19     226699     129401  32169            813           2075        162\n",
       "2020-05-20     227364     132282  32330            665           2881        161\n",
       "2020-05-21     228006     134560  32486            642           2278        156\n",
       "2020-05-22     228658     136720  32616            652           2160        130\n",
       "2020-05-23     229327     138840  32735            669           2120        119"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alternative_italy_data = covid19.get_italy_df()\n",
    "alternative_italy_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 14.4 (lower: 14.26, upper:14.54), CFR via shift and scale: 14.21 (time delay between infection and death: 3.99 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_name, first_date, init_add = 'Italy', pd.to_datetime('2020-02-21'), 0\n",
    "italy_mortality_analysis  = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=init_add, df=alternative_italy_data)\n",
    "italy_mortality_analysis.fit()\n",
    "print(italy_mortality_analysis.print_death_rate())\n",
    "# italy_mortality_analysis2  = covid19.MortalityAnalysis(country_name, first_date=first_date, init_add=2000, mult=4.0)\n",
    "# italy_mortality_analysis2.fit()\n",
    "# print(italy_mortality_analysis2.death_rate())\n",
    "# print(italy_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "italy_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>226699</td>\n",
       "      <td>129401</td>\n",
       "      <td>32169</td>\n",
       "      <td>813</td>\n",
       "      <td>2075</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>227364</td>\n",
       "      <td>132282</td>\n",
       "      <td>32330</td>\n",
       "      <td>665</td>\n",
       "      <td>2881</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>228006</td>\n",
       "      <td>134560</td>\n",
       "      <td>32486</td>\n",
       "      <td>642</td>\n",
       "      <td>2278</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>228658</td>\n",
       "      <td>136720</td>\n",
       "      <td>32616</td>\n",
       "      <td>652</td>\n",
       "      <td>2160</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>229327</td>\n",
       "      <td>138840</td>\n",
       "      <td>32735</td>\n",
       "      <td>669</td>\n",
       "      <td>2120</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "data                                                                            \n",
       "2020-05-19     226699     129401  32169            813           2075        162\n",
       "2020-05-20     227364     132282  32330            665           2881        161\n",
       "2020-05-21     228006     134560  32486            642           2278        156\n",
       "2020-05-22     228658     136720  32616            652           2160        130\n",
       "2020-05-23     229327     138840  32735            669           2120        119"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33023.0</td>\n",
       "      <td>32735</td>\n",
       "      <td>288.0</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>21</td>\n",
       "      <td>6098.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         33023.0        32735        288.0            4640.0                    221.0          21                        6098.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32118.0</td>\n",
       "      <td>31763</td>\n",
       "      <td>355.0</td>\n",
       "      <td>5896.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>21</td>\n",
       "      <td>7749.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         32118.0        31763        355.0            5896.0                    281.0          21                        7749.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# italy_mortality_analysis2.prepend_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>279012</td>\n",
       "      <td>0</td>\n",
       "      <td>27793</td>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>279662</td>\n",
       "      <td>0</td>\n",
       "      <td>27892</td>\n",
       "      <td>650</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>280117</td>\n",
       "      <td>0</td>\n",
       "      <td>27940</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28678</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "index                                                                           \n",
       "2020-05-19     279012          0  27793            464              0        100\n",
       "2020-05-20     279662          0  27892            650              0         99\n",
       "2020-05-21     280117          0  27940            455              0         48\n",
       "2020-05-22          0          0  28628              0              0        688\n",
       "2020-05-23          0          0  28678              0              0         50"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alternative_spain_data = covid19.get_spain_df()\n",
    "alternative_spain_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 10.24 (lower: 10.13, upper:10.35), CFR via shift and scale: 10.67 (time delay between infection and death: 2.53 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spain_mortality_analysis = covid19.MortalityAnalysis('Spain', df=alternative_spain_data)\n",
    "spain_mortality_analysis.fit()\n",
    "print(spain_mortality_analysis.print_death_rate())\n",
    "# spain_mortality_analysis2 = covid19.MortalityAnalysis('Spain', first_date=pd.to_datetime('2020-03-03'), init_add=800, mult=3.0)\n",
    "# spain_mortality_analysis2.fit()\n",
    "# print(spain_mortality_analysis2.death_rate())\n",
    "# print(spain_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "spain_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>279012</td>\n",
       "      <td>0</td>\n",
       "      <td>27793</td>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>279662</td>\n",
       "      <td>0</td>\n",
       "      <td>27892</td>\n",
       "      <td>650</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>280117</td>\n",
       "      <td>0</td>\n",
       "      <td>27940</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28678</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "index                                                                           \n",
       "2020-05-19     279012          0  27793            464              0        100\n",
       "2020-05-20     279662          0  27892            650              0         99\n",
       "2020-05-21     280117          0  27940            455              0         48\n",
       "2020-05-22          0          0  28628              0              0        688\n",
       "2020-05-23          0          0  28678              0              0         50"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spain_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28678</td>\n",
       "      <td>-28678.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0             0.0        28678     -28678.0             839.0                     40.0          21                        1102.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spain_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27977.0</td>\n",
       "      <td>27563</td>\n",
       "      <td>414.0</td>\n",
       "      <td>6742.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8861.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         27977.0        27563        414.0            6742.0                    321.0          21                        8861.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spain_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-19</th>\n",
       "      <td>143427</td>\n",
       "      <td>62563</td>\n",
       "      <td>28022</td>\n",
       "      <td>524</td>\n",
       "      <td>835</td>\n",
       "      <td>-217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-20</th>\n",
       "      <td>143845</td>\n",
       "      <td>63354</td>\n",
       "      <td>28132</td>\n",
       "      <td>418</td>\n",
       "      <td>791</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-21</th>\n",
       "      <td>144163</td>\n",
       "      <td>63858</td>\n",
       "      <td>28215</td>\n",
       "      <td>318</td>\n",
       "      <td>504</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-22</th>\n",
       "      <td>144556</td>\n",
       "      <td>64209</td>\n",
       "      <td>0</td>\n",
       "      <td>393</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-23</th>\n",
       "      <td>144806</td>\n",
       "      <td>64547</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "date                                                                            \n",
       "2020-05-19     143427      62563  28022            524            835       -217\n",
       "2020-05-20     143845      63354  28132            418            791        110\n",
       "2020-05-21     144163      63858  28215            318            504         83\n",
       "2020-05-22     144556      64209      0            393            351          0\n",
       "2020-05-23     144806      64547      0            250            338          0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alternative_france_data = covid19.get_france_df()\n",
    "alternative_france_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFR via Survival analysis: 16.43 (lower: 16.23, upper:16.62), CFR via shift and scale: 21.19 (time delay between infection and death: 5.29 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "france_mortality_analysis = covid19.MortalityAnalysis('France', first_date=pd.to_datetime('2020-02-15'), df=alternative_france_data)\n",
    "france_mortality_analysis.fit()\n",
    "print(france_mortality_analysis.print_death_rate())\n",
    "# france_mortality_analysis2 = covid19.MortalityAnalysis('France', first_date=pd.to_datetime('2020-02-15'), init_add=500, mult=4)\n",
    "# france_mortality_analysis2.fit()\n",
    "# print(france_mortality_analysis2.death_rate())\n",
    "# print(france_mortality_analysis2.prepend_df['confirmed'].iloc[-1])\n",
    "france_mortality_analysis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1517: UserWarning:Exception in curve_fit: Optimal parameters not found: Number of calls to function has reached maxfev = 1000./ None\n",
      " /home/local/cs/workspaces/covid-19-data-analysis/covid19.py:1517: UserWarning:Exception in curve_fit: Optimal parameters not found: Number of calls to function has reached maxfev = 1200./ None\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2560x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "france_mortality_analysis.plot_infection_and_death_curves()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed</th>\n",
       "      <th>recovered</th>\n",
       "      <th>death</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>new_recovered</th>\n",
       "      <th>new_death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>131863</td>\n",
       "      <td>51371</td>\n",
       "      <td>25201</td>\n",
       "      <td>576</td>\n",
       "      <td>587</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>132967</td>\n",
       "      <td>52736</td>\n",
       "      <td>25531</td>\n",
       "      <td>1104</td>\n",
       "      <td>1365</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>137150</td>\n",
       "      <td>53972</td>\n",
       "      <td>25809</td>\n",
       "      <td>4183</td>\n",
       "      <td>1236</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>137779</td>\n",
       "      <td>55027</td>\n",
       "      <td>25987</td>\n",
       "      <td>629</td>\n",
       "      <td>1055</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08</th>\n",
       "      <td>138421</td>\n",
       "      <td>55782</td>\n",
       "      <td>26230</td>\n",
       "      <td>642</td>\n",
       "      <td>755</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed  recovered  death  new_confirmed  new_recovered  new_death\n",
       "date                                                                            \n",
       "2020-05-04     131863      51371  25201            576            587        306\n",
       "2020-05-05     132967      52736  25531           1104           1365        330\n",
       "2020-05-06     137150      53972  25809           4183           1236        278\n",
       "2020-05-07     137779      55027  25987            629           1055        178\n",
       "2020-05-08     138421      55782  26230            642            755        243"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_mortality_analysis.df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| date | note |\n",
    "| --- | --- |\n",
    "| 2020-04-20 | expected_death (17296.0) < today_death(19718); death rate via survival analysis (15.36) << death rate via affine transform of curve estimates (18.7) |\n",
    "| 2020-04-21 | expected_death (17520.0) < today_death(20265); death rate via survival analysis (15.28) << death rate via affine transform of curve estimates (18.9) |\n",
    "| 2020-04-24 | expected_death (19208.0) < today_death(21856); death rate via survival analysis (15.9) << death rate via affine transform of curve estimates (19.2) |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22687.0</td>\n",
       "      <td>26230</td>\n",
       "      <td>-3543.0</td>\n",
       "      <td>5818.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>21</td>\n",
       "      <td>7647.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         22687.0        26230      -3543.0            5818.0                    277.0          21                        7647.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_death</th>\n",
       "      <th>today_death</th>\n",
       "      <th>delta_death</th>\n",
       "      <th>expected_death_2</th>\n",
       "      <th>delta_death_across_days</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>required_ventilator_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20616.0</td>\n",
       "      <td>24760</td>\n",
       "      <td>-4144.0</td>\n",
       "      <td>7843.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>21</td>\n",
       "      <td>10307.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   expected_death  today_death  delta_death  expected_death_2  delta_death_across_days  delta_days  required_ventilator_capacity\n",
       "0         20616.0        24760      -4144.0            7843.0                    373.0          21                       10307.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "france_mortality_analysis.project_death_and_hospitalization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2.6\n"
     ]
    }
   ],
   "source": [
    "import rpy2\n",
    "print(rpy2.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$version.string\n",
      "[1] \"R version 3.6.1 (2019-07-05)\"\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import rpy2.robjects.packages as rpackages\n",
    "baseR = rpackages.importr('base')\n",
    "print(baseR.R_Version().rx('version.string'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from rpy2.rinterface import R_VERSION_BUILD\n",
    "# print(R_VERSION_BUILD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:14: FutureWarning:pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.\n",
      " /home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:34: UserWarning:pandas >= 1.0 is not supported.\n",
      " /home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/lib/ggplot2.py:72: UserWarning:This was designed againt ggplot2 version 3.2.1 but you have 3.3.0\n",
      " /home/local/cs/local/install/anaconda3-5.3.1-Linux-x86_64/envs/py36ds/lib/python3.6/site-packages/rpy2/robjects/vectors.py:927: UserWarning:R object inheriting from \"POSIXct\" but without attribute \"tzone\".\n"
     ]
    }
   ],
   "source": [
    "import IPython.display\n",
    "import rpy2, rpy2.robjects, rpy2.robjects.pandas2ri, rpy2.rinterface, rpy2.robjects.packages, rpy2.interactive, rpy2.robjects.lib.ggplot2, rpy2.robjects.lib.grdevices\n",
    "rpy2.robjects.pandas2ri.activate()\n",
    "\n",
    "from rpy2.robjects.packages import importr\n",
    "# import R's \"base\" package\n",
    "base = importr('base')\n",
    "\n",
    "# import rpy2's package module\n",
    "import rpy2.robjects.packages as rpackages\n",
    "\n",
    "# import R's utility package\n",
    "utils = rpackages.importr('utils')\n",
    "\n",
    "# select a mirror for R packages\n",
    "utils.chooseCRANmirror(ind=1) # select the first mirror in the list\n",
    "\n",
    "# R package names\n",
    "packnames = ('LexisPlotR',)\n",
    "\n",
    "# R vector of strings\n",
    "from rpy2.robjects.vectors import StrVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rpy2.robjects.packages.Package as a <module 'LexisPlotR'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grdevices = rpy2.robjects.packages.importr('grDevices')\n",
    "# Selectively install what needs to be install.\n",
    "# We are fancy, just because we can.\n",
    "names_to_install = [x for x in packnames if not rpackages.isinstalled(x)]\n",
    "if len(names_to_install) > 0:\n",
    "    utils.install_packages(StrVector(names_to_install))\n",
    "\n",
    "lexis = importr('LexisPlotR')\n",
    "lexis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "lexis_grid = rpy2.robjects.r['lexis.grid']\n",
    "lexis_lifeline = rpy2.robjects.r['lexis.lifeline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lexis(mortality_analysis_instance):\n",
    "    mylexis = lexis_grid(year_start = 2020, year_end = 2021, age_start = 0, age_end = 1) #  lwd = 0.1\n",
    "\n",
    "    alpha = 1.0\n",
    "    ix_present = ~mortality_analysis_instance.df_lifelines_individual.observed_death\n",
    "    ix_lost    = mortality_analysis_instance.df_lifelines_individual.observed_death\n",
    "    mylexis = lexis_lifeline(lg = mylexis , entry = mortality_analysis_instance.df_lifelines_individual['start_date'][ix_present], exit = mortality_analysis_instance.df_lifelines_individual['end_date'][ix_present], colour = \"orange\", alpha = alpha, lwd = 0.4)\n",
    "    mylexis = lexis_lifeline(lg = mylexis , entry = mortality_analysis_instance.df_lifelines_individual['start_date'][ix_lost]   , exit = mortality_analysis_instance.df_lifelines_individual['end_date'][ix_lost]   , colour = \"blue\"  , alpha = alpha, lwd = 0.4, lineends = True)\n",
    "\n",
    "    with rpy2.robjects.lib.grdevices.render_to_bytesio(grdevices.png, width=1.5*1024, height=1.5*896, res=90) as img:\n",
    "        rpy2.robjects.r.print(mylexis)   \n",
    "    IPython.display.display(IPython.display.Image(data=img.getvalue(), format='png', embed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_lexis(italy_mortality_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_lexis(italy_mortality_analysis2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lexis(south_korea_mortality_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lexis(germany_mortality_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lexis(france_mortality_analysis)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}